Choo-Yee Ting

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Network intrusion detection research work that employed KDDCup 99 dataset often encounter challenges in creating classifiers that could handle unequal distributed attack categories. The accuracy of a classification model could be jeopardized if the distribution of attack categories in a training dataset is heavily imbalanced where the rare categories are(More)
Problem statement: Implementing a single or multiple classifiers that involve a Bayesian Network (BN) is a rising research interest in network intrusion detection domain. Approach: However, little attention has been given to evaluate the performance of BN classifiers before they could be implemented in a real system. In this research, we proposed a novel(More)
  • Choo-Yee Ting, Yen-Kuan Chong, Wen-Fong Ooi, Boon-Siang Tan, Shang-Jun Chuah, Kee-Leng Saw
  • 2005
Authoring tools that allow content creators to easily create SCORM learning contents, generate multiple lesson plans, and subsequently predict learner's performance from the generated lesson plans have never been an easy task. In this light, we first discuss the overview development of eStoryBoard authoring tool, and subsequently a methodology approach for(More)